{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MNZPDJC4UTBZ6AM4M7FL66AGRO","short_pith_number":"pith:MNZPDJC4","schema_version":"1.0","canonical_sha256":"6372f1a45ca4c39f019c67cabf78068b82c15afe60b2c4522fc4cdc93371088e","source":{"kind":"arxiv","id":"1906.05612","version":1},"attestation_state":"computed","paper":{"title":"Antonym-Synonym Classification Based on New Sub-space Embeddings","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Muhammad Asif Ali, Wei Wang, Xiang Zhao, Xiaoling Zhou, Yifang Sun","submitted_at":"2019-06-13T11:46:12Z","abstract_excerpt":"Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.05612","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-13T11:46:12Z","cross_cats_sorted":[],"title_canon_sha256":"f34129b4ed5538b45ad8ea3ccc74c62cc6775d37aed40ab2c839a48dd303b4dd","abstract_canon_sha256":"57381308a8f2d4f10c7215582e577b01cfb59ed6fab8be341dc1a617101e774d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:24.810765Z","signature_b64":"+g/pKPACWGfnd3IPi9WbfyRLbdVBJtoMGI66mU51gBOhHt0Y1dQf46EaU+lbtGopHU2bnC4K/QnrL5JXuKtsBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6372f1a45ca4c39f019c67cabf78068b82c15afe60b2c4522fc4cdc93371088e","last_reissued_at":"2026-05-17T23:43:24.810291Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:24.810291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Antonym-Synonym Classification Based on New Sub-space Embeddings","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Muhammad Asif Ali, Wei Wang, Xiang Zhao, Xiaoling Zhou, Yifang Sun","submitted_at":"2019-06-13T11:46:12Z","abstract_excerpt":"Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05612","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.05612","created_at":"2026-05-17T23:43:24.810403+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.05612v1","created_at":"2026-05-17T23:43:24.810403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05612","created_at":"2026-05-17T23:43:24.810403+00:00"},{"alias_kind":"pith_short_12","alias_value":"MNZPDJC4UTBZ","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"MNZPDJC4UTBZ6AM4","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"MNZPDJC4","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO","json":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO.json","graph_json":"https://pith.science/api/pith-number/MNZPDJC4UTBZ6AM4M7FL66AGRO/graph.json","events_json":"https://pith.science/api/pith-number/MNZPDJC4UTBZ6AM4M7FL66AGRO/events.json","paper":"https://pith.science/paper/MNZPDJC4"},"agent_actions":{"view_html":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO","download_json":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO.json","view_paper":"https://pith.science/paper/MNZPDJC4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.05612&json=true","fetch_graph":"https://pith.science/api/pith-number/MNZPDJC4UTBZ6AM4M7FL66AGRO/graph.json","fetch_events":"https://pith.science/api/pith-number/MNZPDJC4UTBZ6AM4M7FL66AGRO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO/action/storage_attestation","attest_author":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO/action/author_attestation","sign_citation":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO/action/citation_signature","submit_replication":"https://pith.science/pith/MNZPDJC4UTBZ6AM4M7FL66AGRO/action/replication_record"}},"created_at":"2026-05-17T23:43:24.810403+00:00","updated_at":"2026-05-17T23:43:24.810403+00:00"}